where \(\mathcal {N}\)  means the image normalization; \(\mathcal {P}\)  refers to the data augmentation and the test image resizing strategy for the training and testing stage respectively. Due to the differences of \(\mathcal {P}\)  in existing methods, we thus unify the settings of \(\mathcal {P}\)  for fair comparisons following [1]}. In the training stage, the data augmentation mainly involves four steps: i) Randomly scale the original image via the aspect ratio ranging in [0.75, 2.5]; ii) Randomly crop the image patch with the scale of 640 \(\times \)  640.; iii) Randomly rotate the cropped image patch with the angle of [-90\(^{\circ }\) , 90\(^{\circ }\) ]. iv) Randomly flip the image in the horizontal direction with the probability of 0.5. In the testing stage, the test image resizing strategy indicates the short size of \(\widetilde{\textbf {I}}\)  is set to \(s\)  if it is less than \(s\) , while the longer side is not larger than \(2s\) . In the experiments, \(s\)  is set to 512 in default, and we also investigate the influence of performance against the change of \(s\) .
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